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      Comparing the Psychometric Performance of Generic Paediatric Health-Related Quality of Life Instruments in Children and Adolescents with ADHD, Anxiety and/or Depression

      research-article
      1 , 2 , 3 , , 1 , 2 , 3 , 4 , 5 , 2 , 3 , 6 , 1 , 1 , 3 , in collaboration with the Quality Of Life in Kids: Key evidence to strengthen decisions in Australia (QUOKKA) project team
      Pharmacoeconomics
      Springer International Publishing

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          Abstract

          Objective

          The aim of this study was to examine the validity, reliability and responsiveness of common generic paediatric health-related quality of life (HRQoL) instruments in children and adolescents with mental health challenges.

          Methods

          Participants were a subset of the Australian Paediatric Multi-Instrument Comparison (P-MIC) study and comprised 1013 children aged 4–18 years with attention-deficit/hyperactivity disorder (ADHD) ( n = 533), or anxiety and/or depression ( = 480). Participants completed an online survey including a range of generic paediatric HRQoL instruments (PedsQL, EQ-5D-Y-3L, EQ-5D-Y-5L, CHU9D) and mental health symptom measures (SDQ, SWAN, RCADS-25). A subset of participants also completed the HUI3 and AQoL-6D. The psychometric performance of each HRQoL instrument was assessed regarding acceptability/feasibility; floor/ceiling effects; convergent validity; known-group validity; responsiveness and test–retest reliability.

          Results

          The PedsQL, CHU9D, EQ-5D-Y-3L and EQ-5D-Y-5L showed similarly good performance for acceptability/feasibility, known-group validity and convergent validity. The CHU9D and PedsQL showed no floor or ceiling effects and fair–good test–retest reliability. Test–retest reliability was lower for the EQ-5D-Y-3L and EQ-5D-Y-5L. The EQ-5D-Y-3L showed the highest ceiling effects, but was the top performing instrument alongside the CHU9D on responsiveness to improvements in health status, followed by the PedsQL. The AQoL-6D and HUI3 showed good acceptability/feasibility, no floor or ceiling effects, and good convergent validity, yet poorer performance on known-group validity. Responsiveness and test–retest reliability were not able to be assessed for these two instruments. In subgroup analyses, performance was similar for all instruments for acceptability/feasibility, known-group and convergent validity, however, relative strengths and weaknesses for each instrument were noted for ceiling effects, responsiveness and test–retest reliability. In sensitivity analyses using utility scores, performance regarding known-group and convergent validity worsened slightly for the EQ-5D-Y-3L and CHU9D, though improved slightly for the HUI3 and AQoL-6D.

          Conclusions

          While each instrument showed strong performance in some areas, careful consideration of the choice of instrument is advised, as this may differ dependent on the intended use of the instrument, and the age, gender and type of mental health condition of the population in which the instrument is being used.

          Trial Registration

          ANZCTR—ACTRN12621000657820.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s40273-024-01354-2.

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          Most cited references55

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          A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.

          Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
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            A power primer.

            One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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              Quality criteria were proposed for measurement properties of health status questionnaires.

              Recently, an increasing number of systematic reviews have been published in which the measurement properties of health status questionnaires are compared. For a meaningful comparison, quality criteria for measurement properties are needed. Our aim was to develop quality criteria for design, methods, and outcomes of studies on the development and evaluation of health status questionnaires. Quality criteria for content validity, internal consistency, criterion validity, construct validity, reproducibility, longitudinal validity, responsiveness, floor and ceiling effects, and interpretability were derived from existing guidelines and consensus within our research group. For each measurement property a criterion was defined for a positive, negative, or indeterminate rating, depending on the design, methods, and outcomes of the validation study. Our criteria make a substantial contribution toward defining explicit quality criteria for measurement properties of health status questionnaires. Our criteria can be used in systematic reviews of health status questionnaires, to detect shortcomings and gaps in knowledge of measurement properties, and to design validation studies. The future challenge will be to refine and complete the criteria and to reach broad consensus, especially on quality criteria for good measurement properties.
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                Author and article information

                Contributors
                oloughlin.r@unimelb.edu.au
                Journal
                Pharmacoeconomics
                Pharmacoeconomics
                Pharmacoeconomics
                Springer International Publishing (Cham )
                1170-7690
                1179-2027
                8 February 2024
                8 February 2024
                2024
                : 42
                : Suppl 1
                : 57-77
                Affiliations
                [1 ]Health Economics Unit, School of Population and Global Health, University of Melbourne, ( https://ror.org/01ej9dk98) Melbourne, Victoria 3010 Australia
                [2 ]Health Services Research Unit, The Royal Children’s Hospital, ( https://ror.org/02rktxt32) Parkville, VIC Australia
                [3 ]Health Services and Economics, Murdoch Children’s Research Institute, ( https://ror.org/048fyec77) Parkville, VIC Australia
                [4 ]Centre for Health Economics, Monash University, ( https://ror.org/02bfwt286) Caulfield East, VIC Australia
                [5 ]Centre for Health Economics Research and Evaluation, University of Technology Sydney, ( https://ror.org/03f0f6041) Ultimo, NSW Australia
                [6 ]Department of Paediatrics, University of Melbourne, ( https://ror.org/01ej9dk98) Melbourne, VIC Australia
                Author information
                http://orcid.org/0000-0002-6451-2713
                http://orcid.org/0000-0002-5332-5226
                http://orcid.org/0000-0002-8385-5965
                http://orcid.org/0000-0003-3656-8063
                http://orcid.org/0000-0003-3017-2770
                http://orcid.org/0000-0002-1561-5361
                http://orcid.org/0000-0003-4972-8871
                Article
                1354
                10.1007/s40273-024-01354-2
                11168999
                38329689
                046c1236-a1b1-4615-a452-a8bbc3a53828
                © The Author(s) 2024, corrected publication 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 11 October 2023
                Funding
                Funded by: Medical Research Futures Fund
                Award ID: 1200816
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100006419, EuroQol Research Foundation;
                Award ID: 361-RA
                Award ID: 330-PhD
                Award Recipient :
                Funded by: Australian Government and The University of Melbourne
                Award ID: Research Training Program Scholarship
                Award ID: Research Training Program Scholarship
                Award Recipient :
                Funded by: University of Melbourne
                Categories
                Original Research Article
                Custom metadata
                © Springer Nature Switzerland AG 2024

                Economics of health & social care
                Economics of health & social care

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